A Scalable Preference Model for Autonomous Decision-Making

M Peters, M Saar-Tsechansky, Wolf Ketter, S Williamson, P de Groot, T Heskes

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)
4 Downloads (Pure)

Abstract

Emerging domains such as smart electric grids require decisions to be made autonomously, based on the observed behaviors of large numbers of connected consumers. Existing approaches either lack the flexibility to capture nuanced, individualized preference profiles, or scale poorly with the size of the dataset. We propose a preference model that combines flexible Bayesian nonparametric priors—providing state-of-the-art predictive power—with well-justified structural assumptions that allow a scalable implementation. The Gaussian process scalable preference model via Kronecker factorization (GaSPK) model provides accurate choice predictions and principled uncertainty estimates as input to decision-making tasks. In consumer choice settings where alternatives are described by few key attributes, inference in our model is highly efficient and scalable to tens of thousands of choices.
Original languageEnglish
Pages (from-to)1039-1068
Number of pages30
JournalMachine Learning
Volume107
Issue number6
DOIs
Publication statusPublished - 1 May 2018

Fingerprint

Dive into the research topics of 'A Scalable Preference Model for Autonomous Decision-Making'. Together they form a unique fingerprint.

Cite this